BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization

• URL: http://arxiv.org/abs/2509.11056v1
• Date: Sun, 14 Sep 2025 02:49:29 GMT
• Title: BERT4beam: Large AI Model Enabled Generalized Beamforming Optimization
• Authors: Yuhang Li, Yang Lu, Wei Chen, Bo Ai, Zhiguo Ding, Dusit Niyato,
• Abstract summary: This paper investigates the large-scale AI model designed for beamforming optimization to adapt and generalize to diverse tasks defined by system utilities and scales.<n>We propose a novel framework based on bidirectional encoder representations from transformers (BERT), termed BERT4 encoder.<n>Based on the framework, we propose two BERT-based approaches for single-task and multi-task beamforming optimization, respectively.
• Score: 77.17508487745026
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Artificial intelligence (AI) is anticipated to emerge as a pivotal enabler for the forthcoming sixth-generation (6G) wireless communication systems. However, current research efforts regarding large AI models for wireless communications primarily focus on fine-tuning pre-trained large language models (LLMs) for specific tasks. This paper investigates the large-scale AI model designed for beamforming optimization to adapt and generalize to diverse tasks defined by system utilities and scales. We propose a novel framework based on bidirectional encoder representations from transformers (BERT), termed BERT4beam. We aim to formulate the beamforming optimization problem as a token-level sequence learning task, perform tokenization of the channel state information, construct the BERT model, and conduct task-specific pre-training and fine-tuning strategies. Based on the framework, we propose two BERT-based approaches for single-task and multi-task beamforming optimization, respectively. Both approaches are generalizable for varying user scales. Moreover, the former can adapt to varying system utilities and antenna configurations by re-configuring the input and output module of the BERT model, while the latter, termed UBERT, can directly generalize to diverse tasks, due to a finer-grained tokenization strategy. Extensive simulation results demonstrate that the two proposed approaches can achieve near-optimal performance and outperform existing AI models across various beamforming optimization tasks, showcasing strong adaptability and generalizability.

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